class_docstr: This class implements a convolutional encoder to extract classification embeddings from logspectra.
  
  Arguments
  ---------
  dim : int
      Number of channels of the extracted embeddings.
  
  Returns
  --------
  Latent representations to feed inside classifier and/or intepreter.
  
  Example:
  --------
  >>> inputs = torch.ones(3, 431, 513)
  >>> model = Conv2dEncoder_v2()
  >>> print(model(inputs).shape)
  torch.Size([3, 256, 26, 32])
docstr: Extracts embeddings from logspectrograms.
docstr_tok: ["Extracts","embeddings","from","logspectrograms","."]
code: def __init__(self, dim=256):
      """
      Extracts embeddings from logspectrograms.
      """
      super().__init__()
      self.conv1 = nn.Conv2d(1, dim, 4, 2, 1)
      self.bn1 = nn.BatchNorm2d(dim)
      self.conv2 = nn.Conv2d(dim, dim, 4, 2, 1)
      self.bn2 = nn.BatchNorm2d(dim)
      self.conv3 = nn.Conv2d(dim, dim, 4, 2, 1)
      self.bn3 = nn.BatchNorm2d(dim)
      self.conv4 = nn.Conv2d(dim, dim, 4, 2, 1)
      self.bn4 = nn.BatchNorm2d(dim)
  
      self.resblock = ResBlockAudio(dim)
      self.nonl = nn.ReLU()
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
label_desc: Use image node_id 1 to create a new Conv2dEncoder_v2 object from inherited base classes: nn with example: obj = Conv2dEncoder_v2(dim)